Comprehensive identification of sphingolipid species by in silico retention time and tandem mass spectral library

Additional file 1. Figure S1 The fragment assignments of 12 sphingolipid classes. The annotations were combinatorially performed by hydrogen rearrangement rules in combination with substantial manual curation. The original spectra were obtained from LC/MS data of some biological samples including human cells, mouse tissues, and plant species

Advances in lipidomics

The present article examines recently published literature on lipids, mainly focusing on research involving glycero-, glycerophospho- and sphingo-lipids. The primary aim is identification of distinct profiles in biologic lipidomic systems by ultra-high-performance liquid chromatography (UHPLC) coupled with mass spectrometry (MS, tandem MS) with multivariate data analysis. This review specifically targets lipid biomarkers and disease pathway mechanisms in humans and artificial targets. Different specimen matrices such as primary blood derivatives (plasma, serum, erythrocytes, and blood platelets), faecal matter, urine, as well as biologic tissues (liver, lung and kidney) are highlighted.Peer reviewe

Advances in analytical tools and current statistical methods used in ultra-high-performance liquid chromatography-mass spectrometry of glycero-, glycerophospho- and sphingolipids

The review concentrates on the properties of analytical and statistical ultrahigh-performance liquid chromatographic (UHPLC) - mass spectrometric (MS) methods suitable for glycero-, glycerophospho- and sphingolipids in lipidomics published between the years 2017 2019. Trends and fluctuations of conventional and nano-UHPLC methods with MS and tandem MS detection were observed in context of analysis conditions and tools used for data-analysis. Whereas general workflow characteristics are agreed upon, more details related to the chromatographic methodology (i.e. stationary and mobile phase conditions) need evidently agreements. Lipid quantitation relies upon isotope-labelled standards in targeted analyses and fully standardless algorithm-based untargeted analyses. Furthermore, a wide spectrum of setups have shown potential for the elucidation of complex and large datasets by minimizing the risks of systematic misinterpretation like false positives. This kind of evaluation was shown to have increased importance and usage for cross-validation and data-analysis. (C) 2020 Elsevier B.V. All rights reserved.Peer reviewe

Q-RAI data-independent acquisition for lipidomic quantitative profiling.

Untargeted lipidomics has been increasingly adopted for hypothesis generation in a biological context or discovery of disease biomarkers. Most of the current liquid chromatography mass spectrometry (LC-MS) based untargeted methodologies utilize a data dependent acquisition (DDA) approach in pooled samples for identification and MS-only acquisition for semi-quantification in individual samples. In this study, we present for the first time an untargeted lipidomic workflow that makes use of the newly implemented Quadrupole Resolved All-Ions (Q-RAI) acquisition function on the Agilent 6546 quadrupole time-of-flight (Q-TOF) mass spectrometer to acquire MS2 spectra in data independent acquisition (DIA) mode. This is followed by data processing and analysis on MetaboKit, a software enabling DDA-based spectral library construction and extraction of MS1 and MS2 peak areas, for reproducible identification and quantification of lipids in DIA analysis. This workflow was tested on lipid extracts from human plasma and showed quantification at MS1 and MS2 levels comparable to multiple reaction monitoring (MRM) targeted analysis of the same samples. Analysis of serum from Ceramide Synthase 2 (CerS2) null mice using the Q-RAI DIA workflow identified 88 lipid species significantly different between CerS2 null and wild type mice, including well-characterized changes previously associated with this phenotype. Our results show the Q-RAI DIA as a reliable option to perform simultaneous identification and reproducible relative quantification of lipids in exploratory biological studies

Metabolomics Data Processing and Data Analysis—Current Best Practices

Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows

Lipid metabolite biomarkers in cardiovascular disease: Discovery and biomechanism translation from human studies

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phos-pholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites

Lipid metabolite biomarkers in cardiovascular disease: Discovery and biomechanism translation from human studies

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phos-pholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites

Bioinformatics approaches for the analysis of lipidomics data

The potential impact of lipid research has been increasingly realised both in disease treatment and prevention. Recent advances in soft ionization mass spectrometry (MS) such as electrospray ionization (ESI) have permitted parallel monitoring of several hundreds of lipids in a single experiment and thus facilitated lipidomics level studies. These advances, however, pose a greater challenge for bioinformaticians to handle massive amounts of information-rich MS data from modern analytical instruments in order to understand complex functions of lipids. The main aims of this thesis were to 1) develop bioinformatics approaches for lipid identification based on ultra performance liquid chromatography coupled to mass spectrometry (UPLC/MS) data, 2) predict the functional annotations for unidentified lipids, 3) understand the omics data in the context of pathways and 4) apply existing chemometric methods for exploratory data analysis as well as biomarker discovery. A bioinformatics strategy for the construction of lipid database for major classes of lipids is presented using simplified molecular input line entry system (SMILES) approach. The database was annotated with relevant information such as lipid names including short names, SMILES information, scores, molecular weight, monoisotopic mass, and isotope distribution. The database was tailored for UPLC/MS experiments by incorporating the information such as retention time range, adduct information and main fragments to screen for the potential lipids. This database information facilitated building experimental tandem mass spectrometry libraries for different biological tissues. Non-targeted metabolomics screening is often get plagued by the presence of unknown peaks and thus present an additional challenge for data interpretation. Multiple supervised classification methods were employed and compared for the functional prediction of class labels for unidentified lipids to facilitate exploratory analysis further as well as ease the identification process. As lipidomics goes beyond complete characterization of lipids, new strategies were developed to understand lipids in the context of pathways and thereby providing insights for the phenotype characterization. Chemometric methods such as principal component analysis (PCA) and partial least squares and discriminant analysis (PLS/DA) were utilised for exploratory analysis as well as biomarker discovery in the context of different disease phenotypes

In-quest of Biomarkers For Alzheimer’s Disease And Pharmacokinetic Profile of Anticancer Agents Using Lc-ms In Human Plasma

In this dissertation work, a semi-quantitative assay using a lipidomics approach and an absolute quantitative assay using liquid chromatography-mass spectrometry techniques were developed (LC-MS). In the lipidomics approach, ultra-high pressure liquid chromatography in tandem with quadrupole time of flight (UHPLC-QTOF) mass spectrometry was used to profile, compare and quantitate the human plasma lipids in Alzheimer’s disease subjects (AD) and normal cognitive controls (NCCs). The purpose of this study is to identify potential plasma lipid markers of AD and to explore the relationships between AD and lipid pathways in humans by using bioinformatic tools. The plasma samples of study subjects were first spiked with a mix of 12 synthetic-lipid internal standards, then total lipids were extracted using a modified Blight-Dyer method and fractionated into phospholipids (PL) and neutral lipids (NL) using the aminopropyl cartridge. The UHPLC-QTOF-MS/MS data were processed and analyzed with corrections of retention time and mass shifts. Molecular features were extracted for lipid identifications based on mass-to-charge ratios, isotopic patterns, adducts, and charge states. Venn diagrams were plotted to group the common and the unique features of lipids between AD patients and NCCs. The common significant molecular features between these two study groups were analyzed using principal component analysis (PCA), partial least vii squares-discriminant analysis (PLS-DA), and non-parametric Wilcoxon rank-sum t-test with false discovery rate calculated p-values. Quantitative lipidomics was performed on the twenty-eight identified significant common lipids. Our results indicate these significant common lipids between AD patients and NCCs were belong to glycerophospholipids, glycerolipids, and sphingolipids. Gene-lipid centric pathway analysis was performed on these significant lipids to obtain the implicated pathways in AD and to understand it’s relation to AD. In absolute quantitation work, an assay using a liquid chromatography system in tandem with triple quadrupole mass spectrometry (LC-QqQ) was developed and validated to measure the O6Benzylguanine (O6BG) and its metabolite, 8-oxo-O6Benzylguanine (8-oxoO6BG) in human plasma. O6BG and 8-oxo-O6BG along with the analog internal standard, pCl-O6BG, were extracted from alkalinized human plasma by liquid-liquid extraction (LLE) using ethyl acetate, dried under nitrogen and reconstituted in the mobile phase. Reverse-phase chromatographic separation was achieved using isocratic elution with a mobile phase containing 80% acetonitrile and 0.05% formic acid in water at a flow rate of 0.600 mL/min. Quantification was performed using multiple-reaction-monitoring (MRM) mode with positive ion-spray ionization. The linear calibration ranges of the method for O6BG and 8-oxo-O6BG were 1.25 to 250 ng/mL and 5.00 to 1.00 x 103 ng/mL respectively with acceptable assay accuracy, precision, recovery and matrix factor. The method was validated as per the Food and Drug Administration (FDA) guidelines and was applied to the measurement of O6BG and 8-oxo-O6BG in patient plasma samples from the prior phase I clinical trial

Advanced Mass Spectrometry Techniques for Characterizations of Proteome and Lipidome Profiles of Osteosarcoma Cells

Mass Spectrometry has emerged as an analytical core technique in omics sciences, in particularly mass spectrometry has advanced the fields of proteomics, metabolomics and lipidomics in recent years. Mass spectrometric techniques have enabled the characterization and determination of abundance levels of a wide range of biomolecules at molecular and system-wide scales. This dissertation focuses on the development and application of quantitative mass spectrometry-based methods to decipher the molecular changes associated with hypoxic stress in osteosarcoma